1. Field of the Invention
The present invention generally concerns magnetic resonance tomography (MRT) as used in medicine for examination of patients. The present invention more specifically concerns a method as well as an MRT system for implementation of the method that employ image reconstruction based in partial parallel acquisition (PPA) of the raw data.
2. Description of the Prior Art
MRT is based on the physical phenomenon of magnetic resonance and has been successfully used as an imaging method for over 15 years in medicine and biophysics. In this examination modality, the subject is exposed to a strong, constant magnetic field. The nuclear spins of the atoms in the subject, which were previously randomly oriented, thereby align.
Radio-frequency energy can now excite these “ordered” nuclear spins to a specific oscillation. In MRT, this oscillation generates the actual measurement signal, which is acquired by suitable reception coils. By the use of inhomogeneous magnetic fields generated by gradient coils, the measurement subject can be spatially coded in all three spatial directions. This allows a free selection of the slice to be imaged, so slice images of the human body can be acquired in all directions. MRT as a tomographic image method in medical diagnostics is distinguished predominantly as a “non-invasive” examination method with a versatile contrast capability. Due to the excellent representation of the soft tissue, MRT has developed into a method superior in many ways to x-ray computed tomography (CT). MRT today is based on the application of spin echo and gradient echo sequences that enable an excellent image quality with measurement times in the range of seconds to minutes.
The continuous technical development of the components of MRT apparatuses and the introduction of faster imaging sequences are constantly making more fields of use in medicine amenable to MRT. Real-time imaging to support minimally-invasive surgery, functional imaging in neurology and perfusion measurement in cardiology are only a few examples. In spite of the technical progress in the construction of MRT apparatuses, acquisition time and signal-to-noise ratio (SNR) of an MRT image remain limiting factors for many applications of MRT in medical diagnostics.
Particularly in the case of functional imaging, in which a significant movement of the subject, or parts of the subject, is present (blood flow, heart movement, peristalsis of the abdomen etc.), a reduction of the measurement time (the data acquisition time) is desirable without loss of good SNR. Movement generally causes artifacts in an MRT image such as, for example, movement artifacts that increase with the duration of the data acquisition time. In order to improve the image quality, it would be conceivable to acquire multiple images and to later superimpose these. This does not always lead to an intended improvement of the total image quality, particularly with regard to the movement artifacts. For example, the SNR is improved while the movement artifacts accumulate.
One approach to shorten the measurement time while maintaining good SNR is to reduce the quantity of the acquired image data. In order to acquire a complete image from such a reduced data set, either the missing data must be reconstructed with suitable algorithms or the flawed image from the reduced data must be corrected. The acquisition of the data in MRT occurs in what is known as k-space (spatial frequency domain). The MRT image in the image domain is linked with the MRT data in k-space by means of Fourier transformation. The spatial coding of the subject that spans k-space occurs by means of gradients in all three spatial directions. In the case of 2D imaging, differentiation is made among slice selection (establishes an acquisition slice in the subject, typically the z-axis), frequency coding (establishes a direction in the slice, typically the x-axis) and phase coding (determines the second dimension within the slice, typically the y-axis). These are achieved by respective magnetic field gradients. In the case of 3D imaging, the slice selection is replaced by a second phase coding direction. Without limitation as to generality, a two-dimensional Cartesian k-space is assumed herein that is sampled line-by-line. The data of a single k-space line are frequency-coded by means of a gradient upon readout. Each line in k-space has the interval Δky that is generated by a phase coding step. Since the phase coding takes a great deal of time in comparison with the other spatial codings, methods (for example partial parallel acquisition, (PPA) have been developed that reduce the number of time-consuming phase coding steps, so as to shorten the image measurement time. The fundamental idea of PPA imaging is that the k-space data are not acquired by a single coil, but rather (as shown in FIG. 3A) by a (for example linear) arrangement of component coils (coil 1 through coil 3), namely a coil array. Each of the spatially-independent independent coils of the array carries certain spatial information which is used in order to achieve a complete spatial coding by a combination of the simultaneously-acquired coil data. This means that a number of other unsampled lines 32 (shown dotted in the following figures) that are displaced in k-space can be determined (i.e. reconstructed) from a single acquired k-space line 31 (shown in grey in the following figures). Such completed reconstructed data sets are shown in FIG. 3B for the case of three component coils.
The PPA methods thus use spatial information contained in the components of the coil arrangement in order to partially replace the time-consuming phase coding that is normally done using a phase coding gradient. The image measurement time is thereby reduced corresponding to the ratio of number of the lines of the reduced data set to the number of the lines of the conventional (thus completed) data set. In comparison to conventional data acquisition, in a typical PPA acquisition only a fraction (½, ⅓, ¼, etc.) of the phase coding lines are acquired. A special reconstruction is then applied to the data in order to reconstruct the missing k-space lines and thus to obtain the full field of view (FOV) image in a fraction of the time.
Different PPA methods respectively make use of different reconstruction techniques (normally an algebraic technique). The best known PPA methods are SENSE (sensitivity encoding) and GRAPPA (generalized auto-calibration PPA) with their respective derivatives.
In all PPA methods, additional calibration data points are necessarily also acquired (in addition to the measured central reference lines, for example 33 in FIG. 3) that are added to the actual measurement data, and a reduced data set can actually be completed again only on the basis of these calibration data points.
In order to optimize the quality of the reconstruction and the SNR, a reconstruction according to GRAPPA again generates a number N of data sets (coil images) from, for example, a number N of incompletely measured data sets (except for the reference lines 33, under-sampled coil images; FIG. 2; coil 1 through coil N), which N data sets are—always still in k-space—respectively separately completed again. A Fourier transformation of the individual coil images thus leads to N foldover-free (convolution-free) individual coil images, the combination of which in the spatial domain (for example by a sum-of-squares reconstruction) leads to an image that is optimized with regard to SNR and signal loss.
The GRAPPA reconstruction (FIG. 2), which again leads to N complete individual coil data sets given N component coils, is based on a linear combination of the measured lines of an incomplete data set, with the determination of the (linear) coefficients necessary for this purpose being emphasized. For this purpose it is attempted to linearly combine the regular measured (thus not omitted) lines of an incomplete data set such that the additionally-measured reference lines (thus the calibration data points) can be fitted optimally well. The reference lines thus serve as target functions, the adaptation of which improves as the number of regular measured lines increases. These measured lines may possibly be distributed among incomplete data sets of different component coils.
This means that, in the framework of a GRAPPA reconstruction, the incomplete data sets of N component coils must in turn be mapped to the N component coils to complete these data sets. In the context N GRAPPA input channels are mapped to N GRAPPA output channels. This “mapping” ensues algebraically through a vector matrix multiplication, with the vectors representing the regular measured k-space lines and the matrix representing the determined GRAPPA coefficient matrix. In other words, this means that: if a linear combination of measured lines on the basis of a coefficient matrix results in a good approximation of the reference lines (calibration data points), omitted (and thus not measured) lines of equal number can likewise be reconstructed well with this matrix. The coefficients are often also designated as weighting factors; the reference lines carry information about the coil sensitivities.
It can now be shown that the calculation time for the overall reconstruction method according to GRAPPA (i.e. for the determination of the GRAPPA coefficient matrix as well as for the mapping itself) exhibits a quadratic (in many cases even a “super-quadratic”) dependency on the coil number N which, although not being significant given a lower coil number (8 channels <<1 minute), leads however to unacceptable calculation times with regard to computing capacity and storage capacity of the system computer given a high coil count (N≧32).
In order to satisfy the increasing requirements with regard to CPU load and computer storage in PPA imaging, the focus in this field is presently on more powerful computers with more access and primary memory (RAM), as well as on multiprocessor-based parallel-operating computers that can execute the PPA reconstruction algorithms in parallel. Both approaches represent a substantial cost increase.